Toward Compositional Behavior in Neural Models: A Survey of Current Views

Kate McCurdy, Paul Soulos, Paul Smolensky, Roland Fernandez, Jianfeng Gao


Abstract
Compositionality is a core property of natural language, and compositional behavior (CB) is a crucial goal for modern NLP systems. The research literature, however, includes conflicting perspectives on how CB should be defined, evaluated, and achieved. We propose a conceptual framework to address these questions and survey researchers active in this area.We find consensus on several key points. Researchers broadly accept our proposed definition of CB, agree that it is not solved by current models, and doubt that scale alone will achieve the target behavior. In other areas, we find the field is split on how to move forward, identifying diverse opportunities for future research.
Anthology ID:
2024.emnlp-main.524
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9323–9339
Language:
URL:
https://aclanthology.org/2024.emnlp-main.524
DOI:
10.18653/v1/2024.emnlp-main.524
Bibkey:
Cite (ACL):
Kate McCurdy, Paul Soulos, Paul Smolensky, Roland Fernandez, and Jianfeng Gao. 2024. Toward Compositional Behavior in Neural Models: A Survey of Current Views. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 9323–9339, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Toward Compositional Behavior in Neural Models: A Survey of Current Views (McCurdy et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.524.pdf